Abstract
User Plane Function (UPF) is considered a bridge between User Equipment (UE) and Data Networks (DN) in the 5G core network. A UPF instance can manage multiple Packet Data Unit (PDU) sessions, and there are usually various UPF instances deployed to serve PDU session requests. One requirement is utilizing system resources effectively while ensuring stable system performance. Specifically, the need to optimize unused UPF instances to reduce system costs. The paper proposes a fractional admission controlling (FAC) mechanism and integrates it with a Markov chain-based analytical model for threshold-based scaling for UPF instances (called TSUPF-FAC), in which two additional thresholds are added to control UPF instances globally in order to optimize resource utilization. A threshold-based scaling and fractional admission controlling (TS-FAC) algorithm is developed and implemented in Kubernetes-based Open5GS. The simulation results show a similarity between the analytical and experimental results, in which the analytical model helps to determine the admission thresholds for the best performance of TSUPF-FAC, as measured by metrics such as the number of idle UPF instances and system utilization.